ArticleScientific reports2025
A comprehensive guide to selecting suitable wavelet decomposition level and functions in discrete wavelet transform for fault detection in distribution networks.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- A protection strategy for distribution networks using IoT technology and support vector machine.Scientific reports · 2026Article
- Joint wavelet decomposition of predictors and target variables for drought forecasting.Scientific reports · 2026Article
- Transmission line fault detection and classification using bi-orthogonal wavelet transform (5.5) based signal decomposition.Scientific reports · 2026Article
- High-Precision Waveform Stacking Location Method for Microseismic Events Based on S-Transform.Sensors (Basel, Switzerland) · 2025Article
- A comprehensive EEG dataset and performance assessment for Autism Spectrum Disorder.Scientific reports · 2025Article
- An enhanced protection scheme for power transformers integrating alpha plane analysis.Scientific reports · 2025Article
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4 authors.
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Abstract
The paper presents a comprehensive analysis of the IEEE-16 bus system under different operating conditions. It discusses the selection of suitable decomposition level and wavelet function for analyzing non-stationary signals to enhance power distribution network fault detection. MATLAB/Simulink is used to simulate the system, and transient fault current signals are processed with the MATLAB Wavelet Toolbox. The optimal decomposition level is determined by energy concentration, with the highest energy found in scales D9 (b4), D8 (b5), and D7 (b6), and D8 having the most concentration. Using MATLAB classifier learner, the article evaluates seven common mother wavelets with 53 wavelet functions, and sym3 is found to be the most efficient wavelet function in terms of training time, prediction speed, and accuracy of SVM classifiers. All fault types both symmetrical/unsymmetrical types, and various normal transient conditions such as load/capacitor/DG switching are detected/discriminated with nearly 100% accuracy at the midpoint of line 6-7 with various fault conditions, inception angles (0, 30, 45, 60, 90 and 120°) and a fault resistance of (5,10, 15, and 20 ohms). Additionally, 9 MW wind Farm is integrated at busbar 10, and various fault scenarios are simulated to assess system performance with 100% Accuracy.
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